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Forecasting Agricultural Commodity Prices Using Multivariate Bayesian Machine

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  • Ticlavilca, Andres M.
  • Feuz, Dillon M.

Abstract

The purpose of this paper is to perform multiple predictions for agricultural commodity prices (one, two and three month periods ahead). In order to obtain multiple-time-ahead predictions, this paper applies the Multivariate Relevance Vector Machine (MVRVM) that is based on a Bayesian learning machine approach for regression. The performance of the MVRVM model is compared with the performance of another multiple output model such as Artificial Neural Network (ANN). Bootstrapping methodology is applied to analyze robustness of the MVRVM and ANN.

Suggested Citation

  • Ticlavilca, Andres M. & Feuz, Dillon M., 2010. "Forecasting Agricultural Commodity Prices Using Multivariate Bayesian Machine," 2010 Conference, April 19-20, 2010, St. Louis, Missouri 285320, NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    • Handle: RePEc:ags:nccc10:285320
    DOI: 10.22004/ag.econ.285320
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